Abstract: The Earth offers a multitude of modeling challenges, from the dynamics of the atmosphere and oceans, to the melting of the polar ice caps. To understand and model these climate processes, a wide range of mathematics is needed, such as differential equations, multiscale modeling, and stochastic processes. In this minisymposium, the presentations span a broad range of climate processes and mathematical areas, and will be accessible to a more general audience. They include a blend of modeling, experiments, and data analysis, and demonstrate how mathematics is being employed to address fundamental problems of climate science.

MS-Mo-D-07-113:30--14:00Global Warming: How Can Mathematics Help People to Know It Is Real?Shen, Samuel (San Diego State Univ.)Abstract: Various kinds of climate data from land, ocean, satellite and numerical models can be optimally analyzed using innovative mathematical and statistical methods to demonstrate climate change and highlight natural climate variability, such as El Nino. This lecture will describe how global warming is defined and how the historical global average temperature curves beginning in 1860 from the Intergovernmental Panel on Climate Change were obtained and used as a gauge of global warming.

MS-Mo-D-07-314:30--15:00Progress towards improving seasonal climate prediction by mathematical methods. Tang, Youmin (Univ. of Northern British Columbia)Abstract: In this talk, we will present some progresses in improving seasonal climate predictions by using more advanced mathematical methods. The first example is to rely on the basic properties of stochastic theory to develop an efficient technique for the extraction of climatically relevant singular vectors (CSV) in the presence of weather noise. Emphasis is placed on the applications of the CSV in seasonal climate predictions and to construct optimal ensemble climate predictions. The results indicates that the CSVs can well characterize the optimal error growth of the climate predictions and lead to better ensemble predictions than traditional time lag (TLE) method. The second example is to apply for the information theory to quantify the potential climate predictability. It is found that the information-based measures such as relative entropy and multiple information can better characterize the real predictability than the traditional methods of signal-to-noise ratio. At last, our recent progress in the state estimate of state-space models is discussed with applications of Bayesian-based algorithms. A simplified algorithm of Sigma-point Kalman filter is develop to deal with the state estimation of high-dimensional systems like atmospheric and oceanic general circulation models.

MS-Mo-D-07-415:00--15:30The impact of Southern Ocean storms on sea iceKohout, Alison (NIWA)Abstract: Measurements of wave propagation through Antarctic sea ice are presented. These show that large ocean waves penetrate hundreds of kilometers into the sea ice, further than previously predicted by accepted theory. This implies a more prominent role for ocean waves in sea ice breakup and retreat than previously thought.